Transcriptional Repression by FACT Is Linked to Regulation of Chromatin Accessibility at the Promoter of ES Cells

Published Online: 13 June, 2018 | Supp Info: http://doi.org/10.26508/lsa.201800085 Downloaded from life-science-alliance.org on 26 September, 2021

Research Article

Transcriptional repression by FACT is linked to regulation of chromatin accessibility at the promoter of ES cells

Constantine Mylonas1 , Peter Tessarz1,2

The conserved and essential histone chaperone, facilitates chro- the yeast homolog Pob3. In yeast, an HMG domain protein named matin transcription (FACT), reorganizes nucleosomes during DNA Nhp6 has been proposed to provide the DNA binding capacity of transcription, replication, and repair and ensures both efficient FACT (Formosa et al, 2001). elongation of RNA Pol II and nucleosome integrity. In mammalian The molecular basis for FACT activity has long remained elusive. cells, FACT is a heterodimer, consisting of SSRP1 and SUPT16. Here, However, recent biochemical and structural studies are starting to we show that in contrast to yeast, FACT accumulates at the tran- elucidate how FACT engages nucleosomes (Winkler & Luger, 2011; scription start site of genes reminiscent of RNA polymerase II profile. Hondele et al, 2013; Hsieh et al, 2013; Kemble et al, 2015). Via its Depletion of FACT in mouse embryonic stem cells leads to de- several domains, FACT binds to multiple surfaces on the nucleo- regulation of developmental and pro-proliferative genes concom- some octamer and acts by shielding histone–DNA interactions. itant with hyper-proliferation of mES cells. Using MNase-seq, Assay Initially, it was proposed that FACT would evict an H2A/B dimer from for Transposase-Accessible Chromatin sequencing, and nascent the nucleosome in front of the polymerase and then reinstate elongating transcript sequencing, we show that up-regulation of nucleosome integrity in its wake. However, other data suggest that genes coincides with loss of nucleosomes upstream of the tran- this dimer replacement is not part of FACT function as it leaves the scription start site and concomitant increase in antisense tran- histone composition of the nucleosome intact (Formosa, 2012). scription, indicating that FACT impacts the promoter architecture to Based on recent biochemical data (Hsieh et al, 2013), a model regulate the expression of these genes. Finally, we demonstrate emerges in which RNA Pol II enters the nucleosome and partially a role for FACT in cell fate determination and show that FACT de- uncoils the nucleosomal DNA. At the same time, FACT binds to the pletion primes embryonic stem cells for the neuronal lineage. proximal and distal H2A/H2B dimer, and these FACT–dimer interactions facilitate nucleosome survival. DOI 10.26508/lsa.201800085 | Received 8 May 2018 | Revised 29 May 2018 | Accepted 29 May 2018 | Published online 13 June 2018 Although the genetics and biochemistry of FACT are relatively well understood, it is not known whether cell-type dedicated functions are conferred by this histone chaperone. Interestingly, genome-wide expression analyses across cell and tissue types implicate a role of Introduction FACT in maintaining an undifferentiated state. Depletion of FACT subunits leads to growth reduction in transformed but not in im- The basic functional unit of chromatin is the nucleosome consisting mortalized cells (Garcia et al, 2013), indicating that FACT is essential of around 147 bp of DNA wrapped around an octamer of histone for tumour growth but not for proliferation of untransformed cells. proteins—two copies each of histones H2A, H2B, H3, and H4. In vitro, Finally, FACT regulates the expression of Wnt target genes during chromatinized DNA templates are refractory to transcription, osteoblast differentiation in mesenchymal stem cells and its de- suggesting that the nucleosome might provide a barrier for the letion leads to a differentiation skew (Hossan et al, 2016). Taken elongating RNA polymerase. Using elegant biochemical fraction- together, these data suggested a more specific role for the FACT ation assays coupled to in vitro transcription assays, facilitates complex in undifferentiated cells as previously assumed. chromatin transcription (FACT) was initially characterised as Recent studies have demonstrated that RNA Pol II can transcribe a factor that alleviated the repressive nature of chromatin in vitro in both sense and antisense directions near many mRNA genes (Orphanides et al, 1999). Meanwhile, it has been demonstrated that (Kwak et al, 2013; Mayer et al, 2015). At these so-called bidirectional FACT can cooperate with all RNA polymerases in the cell and ensure promoters, RNA Pol II initiates transcription and undergoes both efficient transcription elongation and nucleosome integrity. promoter-proximal pausing in both the sense (at the protein- Both FACT subunits are highly conserved across all eukaryotes with coding transcription start site [TSS]) and antisense orientation the exception of an HMG-like domain present in SSRP1 but absent in (Kwak et al, 2013; Mayer et al, 2015). Divergent transcription is often

1Max Planck Institute for Biology of Ageing, Cologne, Germany 2Cologne Excellence Cluster on Cellular Stress Responses in Ageing Associated Diseases, University of Cologne, Cologne, Germany

Correspondence: [email protected]

©2018Tessarz and Mylonas https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 1of14 found at mammalian promoters that are rich in CpG content, but actively transcribed genes (Carvalho et al, 2013). We suspect that the lack key core promoter elements such as the TATA motif (Scruggs strong enrichment of FACT subunits around the TSS might mask this et al, 2015). A broader nucleosome free region (NFR) in the promoter potential correlation. Nevertheless, FACT subunits also co-localize region is often accompanied by divergent transcription, and can to the gene body of actively transcribed genes and enrich towards lead to binding of more transcription factors (TFs) resulting in the transcription end site, similarly to H3K36me3 (Fig S1C and D). higher gene activity (Scruggs et al, 2015). Pearson’s correlation among FACT and active marks remained el- Here, we have confirmed an indispensable role of FACT in un- evated when we focused on promoter and enhancer regions (n = differentiated cells based on the expression levels of both FACT 19,461) (Fig 1B). Both subunits displayed very similar binding pattern subunits and, thus, chose mouse embryonic stem cells as a model to to each other over the TSS of all the annotated genes and were investigate how FACT might shape the transcriptome and maintain tightly linked to H3K4me3 levels (Fig 1C). an undifferentiated state. To achieve this, we performed chromatin immunoprecipitation and sequencing (ChIP-seq) and RNA-seq Regulation of gene expression by FACT to identify genes bound and regulated by FACT. To address at a mechanistic level how FACT might regulate transcription in embry- To investigate how FACT orchestrates transcriptional regulation in onic stem (ES) cells, we combined this analysis with MNase digestion mESCs, we depleted SSRP1 levels using short hairpin RNAs (shRNA; Fig of chromatin coupled to deep sequencing (MNase-seq), assay for S2A). Importantly, this also led to a simultaneous depletion of SUPT16 transposase-accessible chromatin using sequencing (ATAC-seq), levels as assessed by mass spectrometry (Table S6). This in- and nascent elongating transcript sequencing (NET-seq). Using terdependence of the two FACT subunits has been observed before these approaches, we have identified a specific gene cluster com- (Garcia et al, 2013). Surprisingly, we observed an increase in mESC prising genes involved in embryogenesis/neuronal development proliferation following Ssrp1 knockdown (KD) as measured by pro- that are up-regulated upon FACT depletion. In addition, we observed liferation rate via metabolic activity measurement (MTT) cell pro- a concomitant increase in chromatin accessibility around the TSS, liferation assays using independent shRNAs (Figs 2A and S2B). This is suggesting that maintenance of nucleosomes at this position by FACT in contrast to previously published data from tumour cell lines, in is part of the mechanism how FACT impacts on the regulation of which proliferation rates decrease, and also from terminally differ- these genes. Finally, our data support a role of FACT in the main- entiated cells, where FACT depletion has no effect on proliferation tenance of a pluripotent state by showing that its depletion leads to (Garcia et al, 2013). Subsequently, we sequenced the whole tran- faster differentiation into the neuronal lineage. scriptome (RNA-seq). In total, we characterized 3,003 differentially expressed genes: 1,655 down-regulated and 1,348 up-regulated (Fig 2B). Down-regulated genes were overrepresented for pathways involved in development, whereas up-regulated genes were involved in Results metabolic processes and positive regulation of proliferation (Fig 2C), indicating that the change in the transcriptome accounts for the faster Occupancy of FACT correlates with marks of active gene proliferation rates. These results suggest that FACT impacts de- expression velopmental processes and negatively controls cell proliferation in mES cells by controlling gene expression patterns. A low correlation High expression of FACT has been associated with stem or less- (Pearson’s R = 0.11) was observed between the coverage of SSRP1 differentiated cells (Garcia et al, 2011). Indeed, we were able to (ChIP-seq) and the gene fold change (RNA-seq) of those genes in the confirm that low FACT levels correlate with highly differentiated cell Ssrp1 KD (Fig 2D), indicating that FACT binding alone is not a predictor lines as opposed to stem and cancer cells (Fig S1A). In addition, for gene expression changes. Taking these findings together, FACT can differentiation of murine ES cells into terminally differentiated work directly as an enhancer or repressor of transcription in mES cells. cardiomyocytes (Wamstad et al, 2012) reveals that FACT levels di- Given the high correlation of FACT with H3K4me3 ( Fig 1A)andto minish throughout the course of differentiation (Fig S1B). Thus, we understand how the transcriptional changes might be linked to chose to explore how FACT contributes to the transcriptome of differences in recruitment of transcriptional regulators, we per- undifferentiated cells using mouse ES cells. Initially, we applied to formed an IP for H3K4me3 followed by mass spectrometry both in mESCs a ChIP-seq assay to identify potential DNA-binding regions control and SSRP1-depleted ES cells (Fig S3A and Table S5). We for both FACT subunits. Subsequently, we examined FACT co- observed an increased binding of Oct4 and Sox2 to H3K4me3 in the enrichment with several other TFs, histone marks, and chromatin Ssrp1 KD state, in line with the observation that FACT depletion remodellers over the gene body area of all uniquely annotated impacts developmental processes. Interestingly, we observed re- protein-coding genes (n = 11,305). High correlation scores were duced binding of many splicing factors on H3K4me3 in the absence of observed between SSRP1, SUPT16, H3K4me3, H3K27ac, and Pol II FACT (Fig S3A). Differential splicing analysis between control and variants (Pol II S5ph and Pol II S2ph), confirming the role of FACT in Ssrp1 KD conditions confirmed in total 356 exon skipping/inclusion active gene expression (Figs 1A and S1E). A good correlation was and 97 intronic retention events following FACT depletion, of which also observed between both FACT subunits and Chd1, in line with around 50% are direct targets of SSRP1 (Fig S3B and C). However, at data demonstrating physical interaction and co-localization in present, it is not clear whether the effects on splicing factor binding mammalian cells (Kelley et al, 1999). However, only a moderate and splicing pattern are directly and mechanistically coupled to FACT correlation was observed between FACT and H3K36me3 on a genome- depletion. Interestingly, a fraction of the differential gene isoforms wide level despite the fact that H3K36me3 directly recruits FACT to generated in the Ssrp1 KD group is overrepresented in limbic system

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 2of14 Figure 1. Correlated occupancies across FACT-bound regions. (A) Heatmap representing Pearson’s correlation between FACT subunits (SSRP1 and SUPT16) and other factors over the gene body area of all uniquely annotated protein- coding genes (n = 11,305). (B) Same as (A) but for promoter/active enhancer regions (n = 19,461) characterized by high H3K27ac and/or Pol II density. (C) Distribution of FACT and other factors (ChIP-seq tags indicated in blue) over the TSS of 11,305 unique RefSeq genes, sorted by H3K4me3 levels. Coinciding RNA expression levels are shown in red. (D) Distribution of several ChIP-seq datasets over a single gene (Egr1). and dendrite development pathways (Fig S3D), suggesting that genes Mononucleosome-sized DNA fragments upon treatment with MNase involved in neuronal development might be influenced by FACT. (135–170 bp) were purified from control and Ssrp1-depleted conditions and sequenced (Fig S4A and B). Nucleosome occupancy was plotted Depletion of FACT induces very specific changes in chromatin for four different gene classes according to the presence of SSRP1 in accessibility the control group (ChIP-Seq) and their relative gene fold change (RNA- seq) in the Ssrp1 KD state. Overall, we observed little changes in Because FACT is responsible for the remodelling of nucleosomes nucleosome occupancy genome wide (Fig 3A). Genes that are down- in front of RNA polymerase and the reestablishment of nucleo- regulatedintheSsrp1 KD (“down-regulated” class) and bound by FACT some integrity in its wake (Formosa, 2012), we speculated whether exhibit a global mononucleosomal shift by a few nucleotides right some of the observed transcriptional alterations could be connected after the +1 nucleosome. Up-regulated genes showed a loss of nu- to changes in nucleosome occupancy upon depletion of FACT. cleosome occupancy in the gene body area regardless of FACT-bound

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 3of14 Figure 2. Regulation of gene expression by FACT. (A) MTT assay assessing cell metabolic activity in mESCs at different cell densities following depletion of FACT levels. Values are mean and SE of three independent transfection experiments are displayed. Signiﬁcance was calculated via a two-tailed t test (P < 0.05). (B) Volcano plot of differentially expressed genes between the control and KD group. Values with logFC > 1 or logFC < −1 and adjusted P-value < 0.01 are highlighted in red. (C) Gene ontology analysis of all differentially expressed genes (red: pathways for down-regulated genes and blue: pathways for up-regulated genes). (D) Scatterplot of log (SSRP1 coverage) (ChIP-seq) over logFC (RNA-seq). Numbers for up-, down-, and non-changing genes are given. Correlation between SSRP1 coverage and gene expression change (fold change) is indicated by the dashed line.

status (non-SSRP1 and SSRP1 targets) (Fig 3A and B). However, spe- paused RNA Pol II on both strands (Seila et al, 2008; Jonkers et al, cifically in up-regulated genes bound by FACT (“up-regulated” class), 2014). In vitro, FACT has been demonstrated to facilitate tran- we observed a significant loss of nucleosomes upstream of the TSS scription through chromatinized templates (Orphanides et al, 1999) (Fig 3A and B). This difference in nucleosome occupancy at the and reduces pausing of the elongating polymerase when it en- promoter region is highly reproducible among the different replicates counters nucleosomes (Hsieh et al, 2013). In yeast, depletion of (Fig S4C). Splitting the up-regulated genes by the amount of H3K4me3 Spt16 leads to up-regulation of antisense transcription from gene- levels (k-means clustering) as a proxy for gene expression levels also internal cryptic promoters (Feng et al, 2016). Thus, to understand revealed that the loss of nucleosomes at the promoter is more how the observed changes in chromatin accessibility would impact profound over the promoters of lowly expressed/repressed genes transcription initiation and to get more mechanistic insight into (control state) (Fig S4C). The observed nucleosome-depleted regions how FACT might dampen expression of genes in mES cells, we were different between up- and down-regulated genes. Such archi- performed NET-seq (Mayer & Churchman, 2016)(Fig S5A), a method tectural differences have been previously attributed to different levels that allows quantitative, strand-specific, and nucleotide resolution of GC frequency. Indeed, GC frequency over SSRP1 targets was higher mapping of RNA Pol II. “ ” and broader in the down-regulated class corroborating a more open Initially, we sought to determine whether nascent transcription chromatin state (Fenouil et al, 2012)(Figs 3A and S4D). positively correlates with mRNA levels. A higher correlation of fi To con rm this difference in chromatin accessibility using an nascent RNA–mRNA expression and a significantly higher slope (P < additional approach, we performed ATAC-seq in control and Ssrp1- 10−5) was observed over SSRP1-target regions in the control state, depleted ES cells (Fig 3C). In line with the observations of the MNase- suggesting higher levels of Pol II pausing and mRNA levels in the fi seq experiments, we observed a statistically signi cant increase (P < presence of FACT (Fig 4A). Nevertheless, in the Ssrp1 KD state, the −10 10 ) in chromatin accessibility in the absence of FACT upstream of SSRP1-bound regions maintained a higher slope, suggesting that the promoter region of FACT-bound, up-regulated genes (Fig 3C–E). In pausing and elongation speed of RNA Pol II are not controlled combination with the RNA-seq data, this reduction in nucleosome entirely by FACT alone (Fig S5B). To confirm this, we measured the occupancy (and subsequently increase in chromatin accessibility) at travelling ratio of RNA Pol II over down-regulated and up-regulated the TSS suggests that FACT might act as a repressor by enabling genes. Indeed, “up-regulated” SSRP1-bound genes show a lower a more closed chromatin conformation state at promoter regions. travelling ratio overall. Interestingly, under our experimental conditions, we did not observe a significant difference among this Gain in chromatin accessibility upon FACT depletion upstream of group of genes following FACT depletion (control to Ssrp1 KD the TSS correlates with an increase in antisense transcription comparison; Fig 4B). Next, we assessed RNA Pol II pausing and directionality over up- Over the last decade, it has become apparent that promoters can regulated genes. NET-seq density plots identified that FACT targets drive expression of sense and antisense RNAs, with proximally displayed higher levels of promoter-proximal RNA Pol II than

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 4of14 Figure 3. Regulation of gene expression by FACT through chromatin accessibility. (A) Nucleosome occupancy of all deregulated genes. Datasets are split by their FACT occupancy status (SSRP1 and non-SSRP1 targets) and their relative transcriptional direction (“down-regulated” and “up-regulated”) following SSRP1 depletion. Solid lines indicate the mean values, whereas the shading represents the SE of the mean. (B)

Boxplots measuring the nucleosome occupancy (log2) over promoters and gene body area of up-regulated genes (**P < 0.001, *P < 0.05, and n.s., not significant). The assessed promoter region is shown in dashed boxes indicated in (A). Significance was calculated using the Welch two-sample t test. (C) Metaplot of open chromatin assessed by ATAC-seq among down-regulated and up-regulated genes both in control and Ssrp1 KD conditions. (D) Cumulative distribution of ATAC-seq density for genes and conditions displayed in (C). Significance was calculated using the Welch two-sample t test (**P <10−9 and n.s., not significant). (E) Interrogation of nucleosome occupancy (MNase-seq) and chromatin accessibility (ATAC-seq) over the Dppa5a gene promoter for control and Ssrp1 KD conditions. Changes in nucleosome occupancy and chromatin accessibility are highlighted in yellow.

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 5of14 Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 6of14 SSRP1-unbound promoters (Fig 4C and D). Upon KD of FACT, SSRP1 KD of FACT. This opening of the promoter is accompanied by an targets displayed an increase (P <10−6) in divergent transcription increase in antisense transcription (Fig 5C). compared with the non-SSRP1 targets (Fig 4E). This occurred pre- We then depleted Ssrp1 levels at the onset of neuronal differ- cisely at locations where nucleosomes were depleted upon KD of entiation and performed immunofluorescence for neurogenesis FACT (Fig 4F). No change in antisense transcription was observed for (β3-tubulin) and dendritic (MAP2) markers at the same time point down-regulated (Fig S6A and B) or unchanged (Fig S6C and D) as the RNA-seq experiment. Ssrp1 KD caused a substantial increase genes, suggesting that the presence of FACT over a specific gene in the expression of those markers as measured by immunofluo- class (up-regulated genes) decreases the rate of antisense tran- rescence, indicating that loss of FACT function primes ES cells for scription by maintaining higher nucleosome density upstream of the neuronal lineage and enhances early neuronal differentiation the TSS. (Fig 5D). A correlation between loss of nucleosomes upstream of the TSS and increase in antisense and sense transcription has recently been reported to occur in mammalian cells (Scruggs et al, 2015). Furthermore, this study showed that antisense Discussion transcription can lead to a more open chromatin structure, enabling increased binding of TFs, which is favourable for sense In this study, we have addressed the role of the histone chaperone fi fi transcription. FACT in mouse ES cells. In contrast to the genomic pro le identi ed for Saccharomyces cerevisiae FACT, where the protein occupancy is depleted at the TSS and accumulates in the gene body (True et al, ES cells differentiate more efficiently into the neuronal lineage 2016), the genomic profile of mammalian FACT over active genes is upon FACT depletion reminiscent of a profile of the Ser5 phosphorylated form of RNA Pol II. This recruitment to the TSS might reflect binding of FACT to RNA Finally, we wanted to investigate whether the transcriptional Pol II. A similar profile for SSRP1 has been reported recently in changes induced by depletion of FACT have physiological conse- HT1080 cells (Garcia et al, 2013). quences. We tested this by differentiating mES cells into the In general, FACT depletion does not lead to gross alterations of neuronal lineage. The rationale for this approach stems from the nucleosomal landscape as measured by MNase- and ATAC-seq. previous studies that pinpoint a specific role for FACT in neurons In particular, genes down-regulated upon FACT depletion only show (Neumüller et al, 2011; Vied et al, 2014) and from the gene ontology a slight shift of nucleosomes, similar to what has been observed in enrichment for neuronal terms that we obtained from mRNA iso- yeast upon FACT inactivation (Feng et al, 2016). It is tempting to form analysis (Fig S3D). We induced differentiation of ES cells to- speculate that the reason for down-regulation lies in the originally wards a neuronal lineage via embryoid body formation and described function of FACT to help passage of RNA Pol II through treatment with retinoic acid (RA) (Bibel et al, 2007). We created chromatin (Orphanides et al, 1999) and its depletion makes this early-stage neural precursor cells (NPCs; 3 d into the differentiation process less efficient. FACT-bound genes that are up-regulated process) and interrogated the whole transcriptome via RNA-seq. We upon Ssrp1 depletion show a significant alteration in nucleoso- identified that in these early-stage NPCs, expression of key neu- mal occupancy around the TSS. FACT depletion leads to loss of rogenesis markers (Pax6, Nes, and Tubb3) increases, whereas FACT nucleosomes and increased rates of bi-directional nascent tran- mRNA levels and key pluripotency factors are yet unchanged and scription, suggesting that these genes are usually dampened or still maintained at a high level (Fig 5A). A quarter of the up- repressed (in case of silent genes) by the maintenance of nucle- regulated genes in ES cells after Ssrp1 KD overlaps with the up- osomes at these sites. The loss of nucleosomal occupancy upon regulated genes instigated by neuronal differentiation (P <10−13, depletion of FACT goes hand-in-hand with an increase in antisense Fisher’s exact test; Fig 5B) and are overrepresented in pathways transcription. Based on the data presented here, we cannot de- involved in neuronal development. Similar to our previous ob- termine if the loss of nucleosomes precedes up-regulation of servations, β3-tubulin (Tubb3) (SSRP1-bound gene), as an example antisense transcription or vice versa. Also, it is not clear whether for neurogenesis genes up-regulated upon FACT depletion, shows this is driven by FACT alone or in combination with RNA polymerase higher chromatin accessibility levels at the promoter region upon and/or chromatin remodellers. However, it is clear that this

Figure 4. Regulation of RNA Pol II directionality by FACT. (A) Scatterplots of log gene body coverage (NET-seq) versus log mRNA expression (RNA-seq) for SSRP1 (n = 4,576) and non-SSRP1 (n = 8,844) target regions in the control state (z-score = 5.3, P <10−5). (B) Measure of Pol II pause/release. Travelling ratio is defined as NET-seq density of proximal promoter versus gene body area. The log-transformed travelling ratio for each gene class is displayed with boxplots. The Welch two-sided t test was used to calculate significance between control and Ssrp1 KD (*P < 0.05, n.s., not significant). (C) NET-seq density plots (control and Ssrp1 KD group) of up-regulated genes split by FACT-bound status (non-SSRP1 and SSRP1 targets). Solid lines indicate mean values, whereas the shading represents the 95% confidence interval. (D) Cumulative distribution of antisense transcription (NET-seq) in a window 1,000 bp upstream of the TSS. The Welch two-sided t test was used to calculate significance between control and Ssrp1 KD among non-SSRP1 and SSRP1 targets. (E) Boxplots assessing fold change (Ssrp1 KD versus control) in antisense transcription (NET-seq) in a window 1,000 bp upstream of the TSS. The Welch two-sided t test was used to calculate significance between non-SSRP1 and SSRP1 targets. (F) Nucleosome occupancy (MNase-seq), open chromatin (ATAC-seq), and transcriptional activity (NET-seq/RNA-seq) over an SSRP1 (Oct4) and non-SSRP1 (Psmb6) target gene between control and Ssrp1 KD conditions. Nucleosomal loss and increase in antisense transcription at the Oct4 promoter is highlighted in yellow.

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 7of14 A B Figure 5. FACT regulates neurogenesis through Pol II/nucleosome dynamics. (A) MA plot depicting differential expression in NPCs versus WT ES cells. Up-regulated genes are highlighted in blue, whereas down-regulated genes are highlighted in red. (B) Venn diagram showing the overlap of up- regulated genes between NPC versus mESCs and control versus Ssrp1 KD mESCs. (C) Interrogation of nucleosome occupancy (MNase-seq), chromatin accessibility (ATAC- seq), and transcriptional activity (NET-seq/RNA-seq) over the Tubb3 gene promoter for control and Ssrp1 KD conditions. Changes in nucleosome occupancy, chromatin accessibility, and Pol II occupancy are highlighted in yellow. (D) Immunoﬂuorescence analysis of early-stage NPCs following Ssrp1 depletion: (blue) DAPI, nuclei; (green) β3-tubulin (Tubb3), neurons; and (red) MAP2, dendrites. Scale is 20 μm.

C D

observed effect is very specific to SSRP1-bound genes that are up- 2009). Taken together, we have shown that FACT can function both regulated upon depletion of FACT. One should note, however, that as an enhancer and a repressor of transcription. The repressive this gene class shows low levels of antisense transcription (Fig 4C function of FACT correlates well with nucleosomal occupancy at the and D). Therefore, one plausible model would be that FACT is re- TSS and suppression of antisense transcription. quired on these promoters to reinstate nucleosomes after initiation FACT expression correlates with the differentiation state of the of antisense transcription. Depletion of FACT would lead to loss of cell, being highest in undifferentiated and lowest in terminally this function and loss of nucleosomes, which in turn would drive differentiated cells. This cannot be simply explained by differences higher levels of antisense transcription. It is of interest to note that in proliferation rates as, e.g., NIH-3T3 also exhibits low levels of FACT FACT depletion in S. cerevisiae by using a thermosensitive allele of expression but proliferates comparably with mouse ES cells. These spt16 also leads to up-regulation of sense/antisense transcription. observations suggest that FACT assists to maintain a chromatin/ However, this occurs at cryptic promoters within the coding region transcription state that allows self-renewal. Indeed, depletion of the gene because of a defect in reestablishing chromatin of FACT leads to an imbalance of the ES cell transcriptome. On the structure after passage of the elongating polymerase (Feng et al, one hand, pro-proliferative genes are up-regulated and lowly 2016). Given the differences of FACT occupancy between mammals expressed developmental factors are further down-regulated, (this study; Garcia et al, 2013) and yeast (True et al, 2016), this might leading to the hyper-proliferation of ES cells. Moreover, the reflect evolutionary differences between mammalian and yeast FACT-depleted gene signature has a large overlap with gene ex- FACT. pression changes observed upon differentiation into the neuronal This scenario described for mammalian FACT would lead to lineage. Interestingly, a comparison of expression patterns in the a wider NFR and allow more efficient recruitment of TFs and RNA early developing mouse brain identified a set of only 13 genes, polymerase. In addition, the torque generated by two divergently including Ssrp1 with high correlation of expression in the pro- elongating RNA Pol II molecules can create sufficient negative liferating cells of the ventricular zone of the neocortex at early supercoiling density in the DNA between the two promoters, which stages of development (Vied et al, 2014). This is a transient em- is known to increase RNA Pol II transcription efficiency (Seila et al, bryonic layer of tissue containing neural stem cells (Rakic, 2009)

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 8of14 and a place for neurogenesis during development dependent on A combination of two different Ssrp1 shRNAs was used (1 and 2; 3 the Notch pathway (Rash et al, 2011). Similar to our study, hyper- and 4) at a time, and depletion was quantified via western blotting proliferation in a stem cell compartment upon FACT depletion has using a monoclonal anti-Ssrp1 antibody (BioLegend). Anti-α tubulin been observed before. Drosophila neuroblasts hyper-proliferate was used as a reference control. The 1 and 2 combination was used upondeletionofSSRP1,suggestingthatitisinvolvedinthe for subsequent experiments as it yielded higher depletion of SSRP1 regulation of balancing neuroblast self-renewal and differen- levels (Fig S2A and B). 48 h after transfection, puromycin (2 μg/ml) tiation (Neumüller et al, 2011). A very recent report also high- selection was applied for an additional 24-h period, before cell lights the role of FACT in assisting cell fate maintenance. Using collection and RNA preparation. Total RNA was obtained via a genetic screen in Caenorhabditis elegans, all FACT subunits phenol–chloroform extraction (QIAzol Lysis Reagent; QIAGEN) fol- were identified as barriers for cellular reprogramming of germ lowed by purification via Quick-RNA MicroPrep (Zymo Research). cells into the neuronal lineage (Kolundzic et al, 2017 Preprint). Library preparation and ribosomal depletion were performed via Comparable with our results, the authors did not observe major the NEBNext Directional RNA Ultra kit (NEB) and the RiboZero kit chromatin architecture alterations but observed larger colonies (Illumina), respectively, according to the manufacturer’s in- during reprogramming assays in the absence of FACT, indicative of structions. Four different biological replicates (control or SSRP1- higher proliferation rates. In agreement with these reports, our data depleted mESCs) were prepared and processed for transcriptome demonstrate that FACT-depleted ES cells differentiate much more analysis. efficiently into early neuronal precursors. Taken together, the data suggest a role for FACT activity during neuronal differentiation, and the proper levels of FACT might assist in balancing proliferation MTT proliferation assay speed and timing of differentiation processes. 48 h after transfection, different cell densities (3 × 104,2×104, and 1 × 4 Materials and Methods 10 ) were seeded on 96-well plates (Sarstedt) along with puromycin (2 μg/ml). 24 h later, the CellTiter 96 Non-Radioactive Cell Pro- liferation Assay kit (Promega) was used according to the manufac- Cell culture turer’s instructions to assess the rate of cell proliferation between the two conditions (control and Ssrp1 KD). Statistical analysis was The E14 cell line (mESCs) was cultured at 37°C, 7.5% CO , on 0.1% 2 performed using a two-tailed ttest. gelatin coated plates, in DMEM + GlutaMax (Gibco) with 15% fetal bovine serum (Gibco), MEM nonessential amino acids (Gibco), penicillin/streptomycin (Gibco), 550 μM 2-mercaptoethanol (Gibco), Transcriptome analysis in SSRP1-depleted mESCs and 10 ng/ml of leukaemia inhibitory factor (eBioscience). HEK293T, N2a, MEFs, NIH3T3, and B16 cell lines were cultured at 37°C, 5% CO 2 Sequenced reads were aligned to the mm10 genome via STAR in DMEM + GlutaMax (Gibco) with 10% fetal bovine serum (Gibco), (v 2.4.1b) (Dobin et al, 2013). Gene and exon counts were obtained and penicillin/streptomycin (Gibco). Early NPCs were generated as from featureCounts of the Rsubread package (R/Bioconductor). previously described (Bibel et al, 2007). Briefly, embryoid bodies Only reads with counts per million > 1 were kept for subsequent were created with the hanging drop technique and were further analysis. Counts were normalised using the internal TMM nor- treated with 1 μM RA for 4 d. RA-treated embryoid bodies were malisation in edgeR (Robinson et al, 2009) and differential ex- trypsinised and cultured in DMEM + GlutaMax (Gibco) with 15% fetal pression was performed using the limma (Ritchie et al, 2015) bovine serum without leukaemia inhibitory factor for 3 d. package. All of the RNA-seq data presented in this article have been normalised to the total library size. Significant genes with an ab- Depletion of SSRP1 from mESCs via shRNA and RNA preparation solute logFC > 1 and adjusted P-value < 0.01 were considered as differentially expressed (Table S1). The “unchanged” gene class (n = E14 were transfected with lentiviral vectors containing either 2,179) was obtained from genes with an adjusted P-value > 0.05. The a scramble control or Ssrp1 shRNAs (MISSION shRNA; Sigma- diffSplice function implemented in limma was used to identify Aldrich) with the following sequences: differentially spliced exons between the two conditions (Table S2). Sequences for E14 transfection. Significant exons with an FDR < 0.001 were considered as differentially spliced. Retention introns were identified using the MISO Scramble CCGGGCGCGATAGCGCTAATAATTTCTCGAGAAATTATTAGCGCTA (Mixture of Isoforms) (Katz et al, 2010) probabilistic framework control TCGCGCTTTTT (Table S3). shRNA 1 CCGGCCTACCTTTCTACACCTGCATCTCGAGATGCAGGTGTAGAAA (Ssrp1) GGTAGGTTTTTG

shRNA 2 CCGGGCGTACATGCTGTGGCTTAATCTCGAGATTAAGCCACAGCAT Retention intron events (Ssrp1) GTACGCTTTTTG

shRNA 3 CCGGGCAGAGGAGTTTGACAGCAATCTCGAGATTGCTGTCAAACTC We veriﬁed the presence of retained introns in the Ssrp1 KD by (Ssrp1) CTCTGCTTTTTG randomly selecting 10 intron retention events. The FastStart SYBR shRNA 4 CCGGCCGTCAGGGTATCATCTTTAACTCGAGTTAAAGATGATACCC Green Master (Roche) was used along with the following primers to (Ssrp1) TGACGGTTTTTG amplify via PCR the retained intragenic regions:

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 9of14 Primers for PCR amplification of the retained intragenic regions. ChIP material was run on a high-sensitivity DNA chip on a 2200 TapeStation (Agilent) to assess size distribution and adaptor Gene name Forward primer Reverse primer contamination. Men1 ATTTCCCAGCAGGCTTCAGG GGGATGACACGGTTGACAGC The samples were single-end deep-sequenced and reads were Dvl1 CCTGGGACTACCTCCAGACA CCTTCATGATGGATCCAATGTA aligned to the mm10 genome using Bowtie2 (v 2.2.6) (Langmead & Map4k2 GCTGCAGTCAGTCCAGGAGG TCCTGTTGCTTCAGAGTAGCC Salzberg, 2012). Peak calling was performed using PePr (v 1.1) (Zhang et al, 2014) with peaks displaying an FDR < 10−5 considered statistically Ctsa GCAATACTCCGGCTACCTCA TGGGGACTCGATATACAGCA significant (Table S4). Peak annotation was performed via the ChIP- Pol2ri CGAAATCGGGAGTGAGTAGC GGTGGAAGAAGGAACGATCA Enrich (Welch et al, 2014) R package with the following parameters Wipf2 TAGAGATGAGCAGCGGAATC TCGAGAGCTGGGGACTTGCA (locusdef = “nearest_gene” andmethod=“broadenrich”). Fuz GACCCAGTGTGTGGACTGTG GACAAAGGCTGTGCCAGTGG Rfx5 CACCAGTTGCCCTCTCTGAA CAATTCTCTTCCTCCCATGC ChIP-seq normalisation and metagene analysis Fhod1 CACCAGGGAGCAGAGATGAT CCATCAACATTGGCCTAACC fi Tcirg1 AGCGACAGCACTCACTCCTT CAACACCCCTGCTTCCAGGC All the ChIP-seq BAM les were converted to bigwig (10 bp bin) and normalised to 1× sequencing depth using deepTools (v 2.4) (Ramirez et al, 2016). Blacklisted mm9 coordinates were converted to mm10 Amplified products were run on a 1.5% agarose gel and visualised using the LiftOver tool from UCSC and were further removed from under UV. Band quantification was performed with ImageJ. the analysis. Average binding profiles were visualised using R (v 3.3.0). Heatmaps were generated via deepTools. For the average ChIP of FACT subunits profiles in Fig S1C and D, RPKM values from control ES RNA-seq data were divided into four different quantiles and the average profile ChIP was performed in ~20 million ES cells, per assay, as described for each FACT subunit was generated for each quantile. The previously (Tessarz et al, 2014) with a few modifications. Briefly, cells Pearson’s correlation plot in Fig 1A was generated using all unique were cross-linked with 1% formaldehyde for 20 min followed by annotated mm10 RefSeq genes (n = 11,305) from UCSC (blacklisted quenching for 5 min with the addition of glycine to a final con- regions were removed). centration of 0.125 M. After washing with PBS buffer, the cells were collected and lysed in cell lysis buffer (5 mM Tris, pH 8.0, 85 mM KCl, MNase-seq following SSRP1 depletion in mESCs and 0.5% NP-40) with proteinase inhibitors (10 μl/ml phenyl- fl μ μ methylsulfonyl uoride, 1 l/ml leupeptin, and 1 l/ml pepstatin). ES cells were cultured and transfected with shRNA vectors as Pellets were spun for 5 min at 5,000 rpm at 4°C. Nuclei were lysed in described above. Biological replicates were obtained from two – nuclei lysis buffer (1% SDS, 10 mM EDTA, and 50 mM Tris HCl) and independent transfection experiments for each shRNA vector. samples were sonicated for 12 min. The samples were centrifuged Briefly, ~5 million cells were cross-linked with 1% formaldehyde for for 20 min at 13,000 rpm at 4°C and the supernatant was diluted 20 min followed by quenching for 5 min with the addition of glycine in IP buffer (0.01% SDS, 1.1% Triton-X-100, 1.2 mM EDTA, 16.7 mM to a final concentration of 0.125 M. After washing with PBS buffer, the – Tris HCl, and 167 mM NaCl), and the appropriate antibody was cells were collected and lysed in cell lysis buffer (5 mM Tris, pH 8.0, added and left overnight with rotation at 4°C. Anti-Ssrp1 and anti- 85 mM KCl, and 0.5% NP-40) with proteinase inhibitors (10 μl/ml Supt16 antibodies were purchased from BioLegend (#609702) and phenylmethylsulfonyl fluoride, 1 μl/ml leupeptin, and 1 μl/ml γ Cell Signalling (#12191), respectively. Anti-AP-2 (Tfap2c) antibody pepstatin). Nuclei were gathered by centrifugation (5,000 rpm for was purchased from Santa Cruz (#sc-12762). Two biological repli- 2 min) and were treated with 10 Kunitz units/106 cells of micro- cates were prepared for each FACT subunit using independent cell coccal nuclease (#M0247S; NEB) for 5 min at 37°C in 40 μl of mi- cultures and chromatin precipitations. Protein A/G Dynabeads crococcal nuclease buffer (#M0247S; NEB). The reaction was (Invitrogen) were added for 1 h and after extensive washes, the stopped with the addition of 60 μl 50 mM EDTA, 25 μl 5 M NaCl, samples were eluted in elution buffer (1% SDS and 0.1 M NaHCO3). and 15 μl 20% NP-40 and incubated on a rotator for 1 h at room μ 20 l of 5 M NaCl was added and the samples were reverse cross- temperature to release soluble nucleosomes. The samples were – linked at 65°C for 4 h. Following phenol chloroform extraction and centrifuged for 5 min at 10,000 g and the supernatant was trans- ethanol precipitation, DNA was incubated at 37°C for 4 h with RNAse ferred to a new tube. This centrifugation step is important to obtain (Sigma-Aldrich). highly soluble nucleosomes and remove nucleosome–protein complexes, which can raise bias in subsequent data interpretation ChIP-seq library preparation, sequencing, and peak calling (Carone et al, 2014)(Fig S7). The samples were reverse cross-linked by incubating overnight at 65°C with 0.5% SDS and proteinase K. Approximately 10–20 ng of ChIP material was used for library Following phenol–chloroform extraction and ethanol precipitation, preparation. End repair and adaptor ligation was prepared as DNA was incubated at 37°C for 4 h with RNAse (Sigma-Aldrich). All described previously with a few modifications (Tessarz et al, 2014). sampleswererunina2%agarosegelandfragments<200bp Double-sided size selections (~200–650 bp) were performed were extracted and purified using the NucleoSpin Gel and PCR using the MagSI-NGS Dynabeads (#MD61021; MagnaMedics) Clean-up kit (Macherey-Nagel) according to the manufacturer’s according to the manufacturer’s instructions. Purified adapter-ligated instructions.

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 10 of 14 Purified DNA (500 ng) was used for library preparation as de- LC-MS/MS analysis scribed above. The only difference was the PCR amplification step where we used the same conditions as mentioned in Henikoff et al For mass spectrometric analysis, the peptides were separated (2011) but with only three amplification cycles. Libraries were online on a 25-cm 75 μm ID PicoFrit analytical column (New Objective) verified using a 2200 TapeStation and were paired-end deep- packed with 1.9 μm ReproSil-Pur media (Dr. Maisch) using an EASY- sequenced (~250 million reads per sample). For quality checks nLC 1000 (Thermo Fisher Scientific). The column was maintained at and reproducibility, please refer to Fig S7. 50°C. Buffer A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The peptides were separated on a segmented gradient from 5% to 25% buffer B for 45 min, from 25% to MNase-seq normalisation and metagene analysis 35% buffer B for 8 min, and from 35% to 45% buffer B for 4 min at 200 nl/min. Eluting peptides were analyzed on a QExactive HF mass All the MNase-seq BAM files were converted to bigwig, binned (1 bp), spectrometer (Thermo Fisher Scientific). Peptide precursor mass to smoothed (20-bp window), and normalised to 1× sequencing depth charge ratio (m/z) measurements (MS1) were carried out at 60,000 using deepTools (v 2.4). Moreover, they were split into two different resolution in the 300 to 1,500 m/z range. The top 10 most intense categories according to fragment length: <80 bp TF-sized fragments precursors with charge state from two to seven only were selected for and 135–170 bp mononucleosome fragments. Average nucleosome HCD fragmentation using 27% collision energy. The m/z of the occupancy profiles were visualised using R (v 3.3.0). For the Fig S7D peptide fragments (MS2) were measured at 15,000 resolution, using and E, the mm10 annotated exon list for mononucleosomal pro- an AGC target of 1e6 and 80 ms maximum injection time. Upon filing was obtained from UCSC. fragmentation, the precursors were put on an exclusion list for 45 s.

ATAC-seq following SSRP1 depletion in mESCs LC-MS/MS data analysis

ES cells were cultured and transfected with shRNA vectors as The raw data were analysed with MaxQuant (Cox & Mann, 2008) described above. Biological replicates were obtained from two (v 1.5.2.8) using the integrated Andromeda search engine (Cox et al, independent transfection experiments for each shRNA vector. 2011). Fragmentation spectra were searched against the canonical ATAC-seq was performed on 50,000 cells as previously described and isoform sequences of the mouse reference proteome (proteome (Buenrostro et al, 2013). All samples were PCR amplified for nine ID: UP000000589, downloaded in August 2015) from UniProt. The cycles and were paired-end sequenced on an Illumina HiSeq 2500 database was automatically complemented with sequences of platform. contaminating proteins by MaxQuant. For the data analysis, methi- onine oxidation and protein N-terminal acetylation were set as variable modifications. The digestion parameters were set to “spe- ATAC-seq normalisation and metagene analysis cific” and “Trypsin/P,” allowing for cleavage after lysine and arginine, also when followed by proline. The minimum number of peptides Sequenced paired mates were mapped on mm10 genome build and razor peptides for protein identification was 1; the minimum using Bowtie2 with the following parameters: –X 2000. Reads cor- number of unique peptides was 0. Protein identification was per- responding to NFRs were selected via a random forest approach formed at a peptide spectrum match and protein false discovery rate using the “ATACseqQC” R package. All the ATAC-seq BAM files were of 0.01. The “second peptide” option was on to identify co-fragmented converted to bigwig, binned (1 bp), and normalised to 1× sequencing peptides. Successful identifications were transferred between the depth using deepTools (v 2.4). Duplicated reads were removed. different raw files using the “match between runs” option, using Chromatin accessibility profiles were visualised using R (v 3.3.0). a match time window of 0.7 min. Label-free quantification (LFQ) (Cox et al, 2014) was performed using an LFQ minimum ratio count of 2. Mass spectrometry sample preparation and analysis Identification of co-enriched proteins Nuclei were isolated from ~5 million ES cells under hypotonic conditions and the samples were incubated overnight at 4°C with an anti- Analysis of the LFQ results was carried out using the Perseus H3K4me3 antibody (#39159; Active Motif) in the presence of low-salt computation platform (Tyanova et al, 2016) (v 1.5.0.0) and R. For the binding buffer (150 mM NaCl, 50 mM Tris–HCl, pH 8.0, and 1% NP-40), analysis, LFQ intensity values were loaded in Perseus and all protease inhibitors, and Protein G Dynabeads (Invitrogen). The fol- identified proteins marked as “Reverse,”“Only identified by site,” and lowing day, after several rounds of bead washing with binding buffer, “Potential contaminant” were removed. Upon log2 transformation of the samples were incubated overnight at 37°C with Tris, pH 8.8, and the LFQ intensity values, all proteins that contained less than four 300 ng Trypsin Gold (Promega). In total, four samples were prepared missing values in one of the groups (control or Ssrp1 KD) were re- for each condition (control and Ssrp1 KD). For the full protein inter- moved. Missing values in the resulting subset of proteins were im- actome of both FACT subunits, nuclei were extracted as descripted puted with a width of 0.3 and down shift of 1.8. Next, the imputed LFQ above, and anti-Ssrp1 and anti-Supt16 antibodies were used. Peptides intensities were loaded into R where a two-side testing for enrich- were desalted using StageTips (Rappsilber et al, 2003) and dried. The ment was performed using limma (Kammers et al, 2015; Ritchie et al, peptides were resuspended in 0.1% formic acid and analyzed using 2015). Proteins with an adjusted P-value < 0.05 were designated as liquid chromatography—mass spectrometry (LC-MS/MS). significantly enriched in the control or knockdown (H3K4me3 IP)

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 11 of 14 (Table S5). The complete list of differential protein expression be- of the two linear regressions was performed by calculating the tween control and Ssrp1 KD can be found in Table S6. z-score by the following equation:

β − β 1 2 NET-seq library preparation z = qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 sβ + sβ 1 2 ES cells were cultured and transfected with shRNA vectors as described above. Biological replicates were obtained from two where β and sβ represent the “slope” and the “standard error of the independent transfection experiments for each shRNA vector. NET- slope,” respectively. P-value was calculated from the respective seq libraries were prepared as previously described (Mayer & confidence level yielded by the z-score. Churchman, 2016) with a few modifications. Briefly, chromatin- associated nascent RNA was extracted through cell fractionation Immunofluorescence and confocal microscopy in the presence of α-amanitin, protease, and RNAase inhibitors. More than 90% recovery of ligated RNA and cDNA was achieved Early NPCs were generated and Ssrp1 levels were knocked down from 15% TBE-Urea (Invitrogen) and 10% TBE-Urea (Invitrogen), as described above. The cells were fixed with 100% ethanol for respectively, by adding RNA recovery buffer (R1070-1-10; Zymo 10 min and processed for immunofluorescence. Permeabilization and Research) to the excised gel slices and further incubating at 70°C blocking was performed for 1 h at room temperature with 1% BSA (1,500 rpm) for 15 min. Gel slurry was transferred through a Zymo- and 0.1% NP-40 in PBS. Incubation with primary antibodies was Spin IV Column (C1007-50; Zymo Research) and further precipitated carried at room temperature for 2 h by using rabbit anti-β3-tubulin for subsequent library preparation steps. cDNA containing the 39 (1:300; Cell Signaling) and mouse anti-MAP2 (1:300; Millipore). After end sequences of a subset of mature and heavily sequenced washing in blocking buffer, the secondary antibodies anti-rabbit snRNAs, snoRNAs, and rRNAs were specifically depleted using and anti-mouse Alexa Fluor 568 (1:1,000; Life Technologies) were biotinylated DNA oligos (Table S7). Oligo-depleted circularised applied for 2 h at room temperature. Slides were extensively cDNA was amplified by PCR (five cycles) and double-stranded DNA washed in PBS and nuclei were counterstained with DAPI before was run on a 4% low melt agarose gel. The final NET-seq library mounting. Fluorescence images were acquired using a laser running at ~150 bp was extracted and further purified using the scanning confocal microscope (TCS SP5-X; Leica), equipped with ZymoClean Gel DNA recovery kit (Zymo Research). Sample purity a white light laser, a 405-diode UV laser, and a 40× objective lens. and concentration was assessed in a 2200 TapeStation and further deep sequenced in a HiSeq 2500 Illumina Platform (~400 million Gene ontology analysis reads per replicate). All GO terms were retrieved from the metascape online platform (http://metascape.org/). NET-seq analysis

Accession numbers and references of publicly available datasets All the NET-seq FASTQ files were processed using custom Python scripts (https://github.com/BradnerLab/netseq) to remove PCR H3K4me3, H3K27me3, Pol II S5ph, H3K4me1, H3K27Ac, and CTCF duplicates and reads arising from RT bias. Reads mapping exactly to (ENCODE Consortium—E14 cell line); Chd1 and Chd2 (de Dieuleveult the last nucleotide of each intron and exon (splicing intermediates) et al, 2016): GSE64825; p53 (Li et al, 2012): GSE26360; and Pol II S2ph were further removed from the analysis. The final NET-seq BAM files (Brookes et al, 2016): GSM850470. Data generated in this study have were converted to bigwig (1 bp bin), separated by strand, and been deposited in Gene Expression Omnibus (GEO) under acces- normalized to 1× sequencing depth using deepTools (v 2.4) with an sion number GSE90906 (ChIP-seq, RNA-seq, chrRNA-seq, MNase-seq, “–offset 1” to record the position of the 59 end of the sequencing ATAC-seq, and NET-seq). read. NET-seq tags sharing the same or opposite orientation with the TSS were assigned as “sense” and “antisense” tags, respectively. Promoter-proximal regions were carefully selected for analysis to ensure that there is minimal contamination from transcription Supplementary Information arising from other transcription units. Genes overlapping within a region of 2.5 kb upstream of the TSS were removed from the Supplementary Information is available at https://doi.org/10.26508/lsa. analysis. For the NET-seq metaplots, genes underwent several 201800085. rounds of k-means clustering to filter regions; in a 2-kb window around the TSS, rows displaying very high Pol II occupancy within a <100-bp region were removed from the analysis as they represent Acknowledgements non-annotated short noncoding RNAs. Average Pol II occupancy profiles were visualised using R (v 3.3.0). In Fig 4B, the proximal We would like to thank Ilian Attanassov of the Max Planck Institute for promoter region was defined as −30 bp and +250 bp around the TSS. Biology of Ageing (MPI-AGE) Proteomics Core Facility for Mass Spectrometry Analysis and the FACS and Imaging Facility for help with microscopy. We are For Fig 4A and B, gene body coverage was retrieved by averaging all particularly grateful to Franziska Metge and Sven Templer (MPI-AGE Bio- regions (FACT-bound and non–FACT-bound) +300 bp downstream informatics Core) for their assistance with coding script formatting. Se- of TSS and −200 bp upstream of transcription end site. Comparison quencing was performed at the Max Planck Genome core centers in Berlin

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 12 of 14 and Cologne, and data analysis was performed on servers of the GWDG, instability in spt16 mutant cells. Mol Cell Biol 36: 1856–1867. Gottingen,¨ and the MPI-AGE cluster. We thank Andy Bannister, Antonis doi:10.1128/mcb.00152-16 Kirmizis, and members of the Tessarz laboratory for discussion and com- Fenouil R, Cauchy P, Koch F, Descostes N, Cabeza JZ, Innocenti C, Ferrier P, Spicuglia ments on the manuscript. This work was funded by the Max Planck Society. S, Gut M, Gut I, et al (2012) CpG islands and GC content dictate nucleosome depletion in a transcription-independent manner at mammalian Author Contributions promoters. Genome Res 22: 2399–2408. doi:10.1101/gr.138776.112 Formosa T (2012) The role of FACT in making and breaking nucleosomes. P Tessarz: conceptualization, supervision, funding acquisition, Biochim Biophys Acta 1819: 247–255. doi:10.1016/j.bbagrm.2011.07.009 project administration, and writing—review and editing. Formosa T, Eriksson P, Wittmeyer J, Ginn J, Yu Y, Stillman DJ (2001) C Mylonas: resources, data curation, software, formal analysis, Spt16–Pob3 and the HMG protein Nhp6 combine to form the – validation, investigation, visualization, and writing—original draft, nucleosome-binding factor SPN. EMBO J 20: 3506 3517. doi:10.1093/emboj/20.13.3506 review, and editing. Garcia H, Fleyshman D, Kolesnikova K, Safina A, Commane M, Paszkiewicz G, Omelian A, Morrison C, Gurova K (2011) Expression of FACT in fl Con ict of Interest Statement mammalian tissues suggests its role in maintaining of undifferentiated state of cells. Oncotarget 2: 783–796. doi:10.18632/oncotarget.340 fl The authors declare that they have no con ict of interest. Garcia H, Miecznikowski JC, Safina A, Commane M, Ruusulehto A, Kilpinen S, Leach RW, Attwood K, Li Y, Degan S, et al (2013) Facilitates chromatin transcription complex is an “accelerator” of tumor transformation and potential marker and target of aggressive cancers. Cell Reports 4: References 159–173. doi:10.1016/j.celrep.2013.06.013 Henikoff JG, Belsky JA, Krassovsky K, MacAlpine DM, Henikoff S (2011) fi Bibel M, Richter J, Lacroix E, Barde Y (2007) Generation of a de ned and Epigenome characterization at single base-pair resolution. Proc Natl uniform population of CNS progenitors and neurons from mouse Acad Sci USA 108: 18318–18323. doi:10.1073/pnas.1110731108 embryonic stem cells. Nature Protocols 2: 1034–1043. doi:10.1038/ nprot.2007.147 Hondele M, Stuwe T, Hassler M, Halbach F, Bowman A, Zhang ET, Nijmeijer B, Kotthoff C, Rybin V, Amlacher S, et al (2013) Structural basis of histone Brookes E, de Santiago I, Hebenstreit D, Morris KJ, Carroll T, Xie SQ, Stock JK, H2A-H2B recognition by the essential chaperone FACT. Nature 499: Heidemann M, Eick D, Nozaki N, et al (2016) Polycomb associates 111–114. doi:10.1038/nature12242 genome-wide with a specific RNA polymerase II variant, and regulates metabolic genes in ESCs. Cell Stem Cell 10: 157–170. doi:10.1016/j. Hossan T, Nagarajan S, Baumgart SJ, Xie W, Magallanes RT, Hernandez C, stem.2011.12.017 Chiaroni PM, Indenbirken D, Spitzner M, Thomas-Chollier M, et al (2016) Histone chaperone SSRP1 is essential for Wnt signaling Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) pathway activity during osteoblast differentiation. Stem Cells 34: Transposition of native chromatin for fast and sensitive epigenomic 1369–1376. doi:10.1002/stem.2287 profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10: 1213. doi:10.1038/nmeth.2688 Hsieh F-K, Kulaeva OI, Patel SS, Dyer PN, Luger K, Reinberg D, Studitsky VM (2013) Histone chaperone FACT action during transcription through Carone BR, Hung JH, Hainer SJ, Chou M Te, Carone DM, Weng Z, Fazzio TG, chromatin by RNA polymerase II. Proc Natl Acad Sci U S A 110: Rando OJ (2014) High-resolution mapping of chromatin packaging in 7654–7659. doi:10.1073/pnas.1222198110 mouse embryonic stem cells and sperm. Dev Cell 30: 11–22. doi:10.1016/j.devcel.2014.05.024 Jonkers I, Kwak H, Lis JT (2014) Promoter-proximal pausing of RNA polymerase II: Emerging roles in metazoans. eLife 3: 1–25. doi:10.7554/elife.02407 Carvalho S, Raposo AC, Martins FB, Grosso AR, Sridhara SC, Rino J, Carmo- Fonseca M, de Almeida SF (2013) Histone methyltransferase SETD2 Kammers K, Cole RN, Tiengwe C, Ruczinski I (2015) Detecting significant coordinates FACT recruitment with nucleosome dynamics during changes in protein abundance. EuPA Open Proteomics 7: 11–19. transcription. Nucleic Acids Research 41: 2881–2893. doi:10.1093/nar/ doi:10.1016/j.euprot.2015.02.002 gks1472 Katz Y, Wang ET, Airoldi EM, Burge CB (2010) Analysis and design of RNA Cox J, Hein MY, Luber CA, Paron I (2014) Accurate proteome-wide label-free sequencing experiments for identifying isoform regulation. Nat quantification by delayed normalization and maximal peptide ratio Methods 7: 1009–1015. doi:10.1038/nmeth.1528 – extraction, termed MaxLFQ. Mol Cell Proteomics 13: 2513 2526. Kelley DE, Stokes DG, Perry RP (1999) CHD1 interacts with SSRP1 and depends doi:10.1074/mcp.m113.031591 on both its chromodomain and its ATPase/helicase-like domain for Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, proper association with chromatin. Chromosoma 108: 10–25. individualized p.p.b.-range mass accuracies and proteome-wide doi:10.1007/s004120050347 fi – protein quanti cation. Nat Biotech 26: 1367 1372. doi:10.1038/nbt.1511 Kemble DJ, McCullough LL, Whitby FG, Formosa T, Hill CP (2015) FACT disrupts Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M (2011) nucleosome structure by binding H2A-H2B with conserved peptide Andromeda: A peptide search engine integrated into the MaxQuant motifs. Mol Cell 60: 294–306. doi:10.1016/j.molcel.2015.09.008 – environment. J Proteome Res 10: 1794 1805. doi:10.1021/pr101065j Kolundzic E, Ofenbauer A, Uyar B, Sommermeier A, Seelk S, He M, Baytek G, de Dieuleveult M, Yen K, Hmitou I, Depaux A, Boussouar F, Dargham DB, Akalin A, Diecke S, Lacadie SA, et al (2017) FACT sets a barrier for cell Jounier S, Humbertclaude H, Ribierre F, Baulard C, et al (2016) fate reprogramming in C. elegans and Human. bioRxiv. Retrieved from Genome-wide nucleosome specificity and function of chromatin http://biorxiv.org/content/early/2017/09/06/185116.abstract – remodellers in ES cells. Nature 530: 113 116. doi:10.1038/nature16505 Kwak H, Fuda NJ, Core LJ, Lis JT (2013) Precise maps of RNA polymerase reveal Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson how promoters direct initiation and pausing. Science 339: 950–953. M, Gingeras TR (2013) STAR: Ultrafast universal RNA-seq aligner. doi:10.1126/science.1229386. Retrieved from http://science. Bioinformatics 29: 15–21. doi:10.1093/bioinformatics/bts635 sciencemag.org/content/339/6122/950.abstract Feng J, Gan H, Eaton ML, Zhou H, Li S, Belsky JA, MacAlpine DM, Zhang Z, Li Q Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. (2016) Non-coding transcription is a driving force for nucleosome Nat Methods 9: 357–359. doi:10.1038/nmeth.1923

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 13 of 14 Li M, He Y, Dubois W, Wu X, Shi J, Huang J (2012) Distinct regulatory Seila AC, Core LJ, Lis JT, Sharp PA (2009) Divergent transcription: A new feature mechanisms and functions for p53-activated and p53-repressed DNA of active promoters. Cell Cycle 8: 2557–2564. doi:10.4161/cc.8.16.9305 – damage response genes in embryonic stem cells. Mol Cell 46: 30 42. Teif VB, Vainshtein Y, Caudron-Herger M, Mallm JP, Marth C, Hofer¨ T, Rippe K doi:10.1016/j.molcel.2012.01.020 (2012) Genome-wide nucleosome positioning during embryonic stem Mayer A, Churchman LS (2016) Genome-wide profiling of RNA polymerase cell development. Nat Struct Mol Biol 19: 1185–1192. doi:10.1038/ transcription at nucleotide resolution in human cells with native nsmb.2419 – elongating transcript sequencing. Nat Protocols 11: 813 833. Tessarz P, Santos-Rosa H, Robson SC, Sylvestersen KB, Nelson CJ, Nielsen ML, doi:10.1038/nprot.2016.047 Kouzarides T (2014) Glutamine methylation in histone H2A is an RNA- Mayer A, Di Iulio J, Maleri S, Eser U, Vierstra J, Reynolds A, Sandstrom R, polymerase-I-dedicated modification. Nature 505: 564–568. Stamatoyannopoulos JA, Churchman LS (2015) Native elongating doi:10.1038/nature12819 transcript sequencing reveals human transcriptional activity at True JD, Muldoon JJ, Carver MN, Poorey K, Shetty SJ, Bekiranov S, Auble DT – nucleotide resolution. Cell 161: 541 544. doi:10.1016/j.cell.2015.03.010 (2016) The modifier of transcription 1 (Mot1) ATPase and Spt16 histone Neumüller RA, Richter C, Fischer A, Novatchkova M, Neumüller KG, Knoblich JA chaperone co-regulate transcription through preinitiation complex (2011) Genome-wide analysis of self-renewal in Drosophila neural assembly and nucleosome organization. J Biol Chem 291: 15307–15319. stem cells by transgenic RNAi. Cell Stem Cell 8: 580–593. doi:10.1016/j. doi:10.1074/jbc.m116.735134 stem.2011.02.022 Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J Orphanides G, Wu WH, Lane WS, Hampsey M, Reinberg D (1999) The chromatin- (2016) The Perseus computational platform for comprehensive specific transcription elongation factor FACT comprises human SPT16 analysis of (prote)omics data. Nat Methods 13: 731–740. doi:10.1038/ and SSRP1 proteins. Nature 400: 284–288. doi:10.1038/22350 nmeth.3901 Rakic P (2009) Evolution of the neocortex: A perspective from developmental Vied CM, Freudenberg F, Wang Y, Raposo A, Feng D, Nowakowski RS (2014) A biology. Nat Rev Neurosci 10: 724–735. doi:10.1038/nrn2719 multi-resource data integration approach: Identification of candidate genes regulating cell proliferation during neocortical development. Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Front Neurosci 8: 257. doi:10.3389/fnins.2014.00257 Dündar F, Manke T (2016) deepTools2: A next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44: 160–165. Voong LN, Xi L, Sebeson AC, Xiong B, Wang JP, Wang X (2016) Insights into doi:10.1093/nar/gkw257 Nucleosome Organization in Mouse Embryonic Stem Cells through Chemical Mapping. Cell 167: 1555–1570.e15. doi:10.1016/j.cell.2016. Rappsilber J, Ishihama Y, Mann M (2003) Stop and go extraction tips for 10.049 matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/ms sample pretreatment in Proteomics. Anal Chem 75: 663–670. Wamstad JA, Alexander JM, Truty RM, Shrikumar A, Li F, Eilertson KE, Ding H, doi:10.1021/ac026117i Wylie JN, Pico AR, Capra JA, et al (2012) Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac Rash BG, Lim HD, Breunig JJ, Vaccarino FM (2011) FGF signaling expands embryonic lineage. Cell 151: 206–220. doi:10.1016/j.cell.2012.07.035 cortical surface area by regulating notch-dependent neurogenesis. J Neurosci 31: 15604–15617. doi:10.1523/jneurosci.4439-11.2011. Retrieved Welch RP, Lee C, Imbriano PM, Patil S, Weymouth TE, Smith RA, Scott LJ, Sartor from http://www.jneurosci.org/content/31/43/15604.abstract MA (2014) ChIP-Enrich: Gene set enrichment testing for ChIP-seq data. Nucleic Acids Res 42: 1–13. doi:10.1093/nar/gku463 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and Winkler DD, Luger K (2011) The histone chaperone FACT: Structural insights microarray studies. Nucleic Acids Res 43: e47. doi:10.1093/nar/gkv007 and mechanisms for nucleosome reorganization. J Biol Chem 286: 18369–18374. doi:10.1074/jbc.r110.180778 Robinson MD, McCarthy DJ, Smyth GK (2009) edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Zhang Y, Lin YH, Johnson TD, Rozek LS, Sartor MA (2014) PePr: A peak-calling Bioinformatics 26: 139–140. doi:10.1093/bioinformatics/btp616 prioritization pipeline to identify consistent or differential peaks from replicated ChIP-seq data. Bioinformatics 30: 2568–2575. doi:10.1093/ Scruggs BS, Gilchrist DA, Nechaev S, Muse GW, Burkholder A, Fargo DC, bioinformatics/btu372 Adelman K (2015) Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol Cell 58: 1101–1112. doi:10.1016/j.molcel.2015.04.006 Seila AC, Calabrese JM, Levine SS, Yeo GW, Rahl PB, Flynn RA, Young RA, Sharp License: This article is available under a Creative PA (2008) Divergent transcription from active promoters. Science 322: Commons License (Attribution 4.0 International, as 1849–1851. doi:10.1126/science.1162253. Retrieved from http://science. described at https://creativecommons.org/ sciencemag.org/content/322/5909/1849.abstract licenses/by/4.0/).

Impact of FACT deletion on transcription Mylonas and Tessarz https://doi.org/10.26508/lsa.201800085 vol 1 | no 3 | e201800085 14 of 14